Product School

5 Ways to Increase Product Engagement Using Advanced Insights


Author: David Geffen

March 6, 2023 - 6 min read

Updated: January 24, 2024 - 6 min read

“Hard numbers tell an important story; user stats and sales numbers will always be key metrics. But your users are sharing a huge amount of qualitative data, too — and a lot of companies either don’t know how or forget to act on it.” - Stewart Butterfield, CEO and Founder, Slack

Product Analytics is the lifeblood of any Product Manager. They rely on it to get their job done: to understand their customers, make informed product decisions, prioritize their time and issues, manage stakeholders, collaborate with teams, and much more. In a digital-first, competitive landscape, the stakes are even higher, and so is the need for deeper insights to make better and faster product decisions.

Product Analytics enable better informed decisions


Product Managers rely on a variety of engagement analytics to track, visualize and analyze user engagement and behavior. The insights can be used to make data-driven decisions and to optimize and improve the product. Access to product data is critical, especially because according to research, data driven businesses are:

  • 23 times more likely to attract customers

  • 6 times more likely to retain customers

  • 19 times more likely to be profitable

Tracking Product Metrics helps Product Managers gain an understanding of how people are using their products, such as which features are used are abandoned, if newly introduced features are quickly adopted or dismissed and what the customer journey looks like.

And Product Metrics can answer questions. Lots of them, like: Why are certain features getting abandoned? Which features are users no longer interested in? Would a specific feature tweak lead to increased revenue? Which customer segment is driving the most revenue growth? Where are most of our customers coming from? 

But there are limitations to standalone Product Analytics solutions.

Product Analytics: A limited view of customer behavior


Not surprisingly, the volume of Product Analytics data can be overwhelming, which led to the creation of AARRR or Pirate Metrics, identifying the most important user behavior that drives engagement and growth. They are:

  • Acquisition: Know how your customers discover your product or company

  • Activation: Understand if the user is becoming a paying customer or active user

  • Retention: Evaluate if the customers are satisfied and staying with the company

  • Referral: Determine whether customers are sharing positive feelings about your products 

  • Revenue: Measure if the product and/or feature is generating money, particularly customer acquisition cost (CAC) and customer lifetime value (CLV)

While Pirate Metrics are the bread and butter of Product Analytics, in some sense, they offer a “surface level” view of customer engagement. Product Analytics answer questions about the what: quantifiable data over a predefined time period for a customer or customer segment across multiple interactions or sessions. For example:

  • How many videos did it take for a shopper to turn into a customer?

  • What are the specific features correlated to a subscriber upgrading their product?

  • Which customer segment is most likely to abandon the product?

And to add to that, Product Analytics capture only the data you define. There isn’t a way to track trends and anomalies to get a richer picture of customer behavior. That’s where Product Analytics stop at delivering customer insights behind the data. For instance:

  • I can see page-level data trends, but don’t know why some users leave after 10 seconds and others browse for 10 minutes.

  • I know how my customer found my product and purchased it, but not the path to purchase.

  • I can see that users in large numbers struggle with this feature but I don’t know the reason nor what they did before or after.

Product analytics vs. digital experience intelligence: What’s the difference?


Digital experience intelligence platforms provide a comprehensive view of every event on a website or mobile app during a single session. Using tagless capture, AI-powered insights and other advanced features, DXI analytics can answer the why, not just the quantitative nature of a standalone product analytics solution. 

This is the biggest difference between a traditional Product Analytics solution and digital experience intelligence: Digital experience intelligence solutions can capture the entire experience of a user over a single or multiple sessions to learn about their behavior, struggles, and decision-making through the entire process. Digital intelligence experience platforms offer Product Managers the new levels of insight into the customer journey as they experience your website or app. 

5 ways advanced product insights can help you increase customer engagement

A digital intelligence experience platform helps Product Managers understand how customers are using and experiencing the functionality of your website or mobile app from the minute you add any capability or feature. This data can be used to:

  1. Understand your customer’s behavior: Measuring engagement with a product feature, you can answer questions like: Where in the product does it make the most sense to position a new site or app capability? Are users struggling with the feature or backtracking or abandoning the site altogether?

  2. Increase engagement and future adoption: It’s not just what a user is doing at that moment, but connecting those decisions made later in the journey. Did they take the planned customer path or did they go to an unexpected screen on the app? Which screen elements drive the highest conversion rate?

  3. Create products that your customers want: Deeper insights create new ideas and opportunities. What new features can we iterate from the success (or failure) of this capability? What experiments and A/B testing can we do to determine the best path forward? 

  4. Prioritize project and team resources: When you know the impact of a feature, it drives the next steps. If you can see customers are having difficulty with the new ‘purchase’ button, should you slot it ahead of fixing load time issues? Should you roll out the new freemium feature when premium usage is at an all-time high?

  5. Identify and eliminate user struggles: Understand where and how your customers are having a negative experience using analytics integrated with other tools in your stack. What voice of customer (VoC) feedback are you getting on a feature that aligns with the customer experience (or doesn’t)? What are interaction and heatmaps telling you about user patterns and issues?

Make no mistake, Product Analytics are an essential tool in the Product Management toolbox. Unifying Product Analytics into a digital experience intelligence platform powers up your existing data and completes the picture. Product Managers can get to the “why” faster and gain the most comprehensive view of the digital customer experience, leading to more product engagement–and yes, conversions. Learn more about how Glassbox can help by visiting

Updated: January 24, 2024

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